{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a346b","dataset_id":"ds006840","associated_paper_doi":null,"authors":["Mengpu Cai","Rongrong Fu","Yaodong Wang","Bin Lu","Saiwei Guo","Fangyao Xu"],"bids_version":"1.7.0","contact_info":["Mengpu Cai"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds006840.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":15,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006840","osf_url":null,"github_url":null,"paper_url":null},"funding":["N/A"],"ingestion_fingerprint":"7dcfeeff8dae078f631e088b49374fa0425720b8d317d7de56d0fb546046e77d","license":"CC0","n_contributing_labs":null,"name":"IACKD: Intention Action Conflict EEG-Hand Kinematics Dataset ","readme":"References\n----------\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8","recording_modality":["eeg"],"senior_author":"Fangyao Xu","sessions":[],"size_bytes":6435718408,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ihc"],"timestamps":{"digested_at":"2026-04-22T12:29:41.599174+00:00","dataset_created_at":"2025-10-25T06:33:21.727Z","dataset_modified_at":"2025-11-17T02:47:29.000Z"},"total_files":128,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006840","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-ihc_events.json"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"e6362e5435b5a448","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Motor"],"type":["Motor"],"confidence":{"pathology":0.6,"modality":0.7,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm is the “EEG Motor Movement/Imagery Dataset” (Schalk et al.). It shows the catalog convention that datasets centered on executed/imagined movements are labeled Type=“Motor”. That example uses Modality=“Visual” because the metadata explicitly includes on-screen targets (“A target appears…”), illustrating that Modality should follow described stimulus channel when available. For the current dataset, stimulus channel is not described, but the title strongly indicates a hand-movement/kinematics paradigm; thus Motor-focused labeling is guided by that motor-task convention, while keeping Modality conservative due to missing stimulus details.","metadata_analysis":"Key available metadata is sparse. Relevant snippets:\n1) Title explicitly indicates a motor/kinematics paradigm: “IACKD: Intention Action Conflict EEG-Hand Kinematics Dataset”.\n2) No clinical population is described; only a count is given: “Subjects: 15”.\n3) Single task label present without description: “tasks: [\"ihc\"]”.\nThere are no statements about diagnosis/patients, and no explicit description of sensory stimuli (visual/auditory/tactile) beyond the motor/hand-kinematics implication of the title.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no diagnosis mentioned; only “Subjects: 15”.\n- Few-shot pattern suggests: when no disorder is mentioned and the dataset is a generic experimental task, label as “Healthy”.\n- Alignment: ALIGN (no conflicting clinical recruitment info).\n\nModality:\n- Metadata says: no explicit stimulus modality; title implies action/hand movement: “EEG-Hand Kinematics”.\n- Few-shot pattern suggests: motor paradigms often have visual cues (hence Modality=Visual in the motor/imagery example), but Modality should track described stimuli; if not described, infer from dominant input/interaction.\n- Alignment: PARTIAL. Few-shot example warns not to assume Visual without explicit evidence; given the explicit motor/kinematics framing, “Motor” modality is the most defensible inference.\n\nType:\n- Metadata says: “Intention Action Conflict” with “EEG-Hand Kinematics” indicates movement/action control as central.\n- Few-shot pattern suggests: movement execution/imagery as primary aim → Type=“Motor”.\n- Alignment: ALIGN.","decision_summary":"Pathology (top-2):\n1) Healthy — Supported by absence of any clinical recruitment language plus generic sample statement “Subjects: 15”.\n2) Unknown — Competing option because no explicit “healthy controls” wording is present.\nChosen: Healthy (metadata is non-clinical and provides no alternative pathology).\n\nModality (top-2):\n1) Motor — Supported by title “EEG-Hand Kinematics” indicating motor action as the dominant interaction; no other stimulus channel described.\n2) Visual — Possible because many motor-conflict tasks use visual cues, and the motor few-shot example is visually cued; however, this dataset lacks any explicit visual-stimulus statement.\nChosen: Motor (avoid assuming visual stimuli without evidence).\n\nType (top-2):\n1) Motor — Supported by the dataset’s focus on action/hand kinematics (“Intention Action Conflict”, “EEG-Hand Kinematics”).\n2) Attention — Possible because “conflict” paradigms can target cognitive control/attention, but no task description is provided to justify that as primary over motor control.\nChosen: Motor.\n\nConfidence justification:\n- Pathology confidence is limited (no explicit healthy statement; only lack of pathology + subject count).\n- Modality and Type rely mainly on the title implying hand movement/kinematics, with no further task/stimulus description."}},"computed_title":"IACKD: Intention Action Conflict EEG-Hand Kinematics Dataset","nchans_counts":[{"val":29,"count":96},{"val":31,"count":32}],"sfreq_counts":[{"val":1024.0,"count":128}],"stats_computed_at":"2026-04-22T23:16:00.312032+00:00","total_duration_s":53213.3125,"canonical_name":null,"name_confidence":0.78,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Cai2025"}}